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Design Process
Modeling
Constraints
E-R Diagram
Design Issues
Weak Entity Sets
Extended E-R Features
Design of the Bank Database
Reduction to Relation Schemas
Database Design
UML
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A database can be modeled as:
◦ a collection of entities,
◦ relationship among entities.
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An entity is an object that exists and is
distinguishable from other objects.
◦ Example: specific person, company, event, plant
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Entities have attributes
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An entity set is a set of entities of the same
type that share the same properties.
◦ Example: people have names and addresses
◦ Example: set of all persons, companies, trees,
holidays
customer_id customer_ customer_ customer_
name street
city
loan_
number
amount
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A relationship is an association among several
entities
Example:
Hayes
depositor
A-102
customer entityrelationship setaccount entity
A relationship set is a mathematical relation
among n  2 entities, each taken from entity
sets
{(e1, e2, … en) | e1  E1, e2  E2, …, en
 En}
where (e1, e2, …, en) is a relationship
◦ Example:
(Hayes, A-102)  depositor
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An attribute can also be property of a relationship set.
For instance, the depositor relationship set between
entity sets customer and account may have the
attribute access-date
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Refers to number of entity sets that participate in a
relationship set.
Relationship sets that involve two entity sets are
binary (or degree two). Generally, most
relationship sets in a database system are binary.
Relationship sets may involve more than two entity
sets.
Example: Suppose employees of a bank may have jobs
(responsibilities) at multiple branches, with different jobs at
different branches. Then there is a ternary relationship set
between entity sets employee, job, and branch
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Relationships between more than two entity sets
are rare. Most relationships are binary. (More on
this later.)
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An entity is represented by a set of attributes,
that is descriptive properties possessed by all
Example: of an entity set.
members
customer = (customer_id, customer_name,
customer_street, customer_city )
loan = (loan_number, amount )
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Domain – the set of permitted values for each
attribute
Attribute types:
◦ Simple and composite attributes.
◦ Single-valued and multi-valued attributes
 Example: multivalued attribute: phone_numbers
◦ Derived attributes
 Can be computed from other attributes
 Example: age, given date_of_birth
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Express the number of entities to which
another entity can be associated via a
relationship set.
Most useful in describing binary relationship
sets.
For a binary relationship set the mapping
cardinality must be one of the following
types:
◦
◦
◦
◦
One to one
One to many
Many to one
Many to many
One to one
One to many
Note: Some elements in A and B may not be mapped to any
elements in the other set
Many to one
Many to many
Note: Some elements in A and B may not be mapped to any
elements in the other set
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A super key of an entity set is a set of
one or more attributes whose values
uniquely determine each entity.
A candidate key of an entity set is a
minimal super key
◦ Customer_id is candidate key of customer
◦ account_number is candidate key of account
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Although several candidate keys may
exist, one of the candidate keys is
selected to be the primary key.
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The combination of primary keys of the
participating entity sets forms a super key of a
relationship set.
◦ (customer_id, account_number) is the super key of
depositor
◦ NOTE: this means a pair of entity sets can have at most
one relationship in a particular relationship set.
 Example: if we wish to track all access_dates to each account
by each customer, we cannot assume a relationship for each
access. We can use a multivalued attribute though
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Must consider the mapping cardinality of the
relationship set when deciding what are the
candidate keys
Need to consider semantics of relationship set in
selecting the primary key in case of more than one
candidate key
 Rectangles represent entity sets.
 Diamonds represent relationship sets.
 Lines link attributes to entity sets and entity sets to relationship sets.
 Ellipses represent attributes
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Double ellipses represent multivalued attributes.
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Dashed ellipses denote derived attributes.
 Underline indicates primary key attributes (will study later)
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Entity sets of a relationship need not be distinct
The labels “manager” and “worker” are called
roles; they specify how employee entities interact
via the works_for relationship set.
Roles are indicated in E-R diagrams by labeling
the lines that connect diamonds to rectangles.
Role labels are optional, and are used to clarify
semantics of the relationship
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We express cardinality constraints by drawing either a
directed line (), signifying “one,” or an undirected line
(—), signifying “many,” between the relationship set and
the entity set.
One-to-one relationship:
◦ A customer is associated with at most one loan via the
relationship borrower
◦ A loan is associated with at most one customer via borrower
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In the one-to-many relationship a loan is
associated with at most one customer via
borrower, a customer is associated with
several (including 0) loans via borrower
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In a many-to-one relationship a loan is
associated with several (including 0) customers
via borrower, a customer is associated with at
most one loan via borrower
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A customer is associated with several
(possibly 0) loans via borrower
A loan is associated with several (possibly
0) customers via borrower
 Total participation (indicated by double line): every entity in the entity set
participates in at least one relationship in the relationship set
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E.g. participation of loan in borrower is total
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every loan must have a customer associated to it via borrower
 Partial participation: some entities may not participate in any relationship in
the relationship set
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Example: participation of customer in borrower is partial
 Cardinality limits can also express participation constraints
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We allow at most one arrow out of a ternary (or
greater degree) relationship to indicate a cardinality
constraint
E.g. an arrow from works_on to job indicates each
employee works on at most one job at any branch.
If there is more than one arrow, there are two ways of
defining the meaning.
◦ E.g a ternary relationship R between A, B and C with arrows
to B and C could mean
1. each A entity is associated with a unique entity from B
and C or
2. each pair of entities from (A, B) is associated with a
unique C entity,
and each pair (A, C) is associated with a
unique B
◦ Each alternative has been used in different formalisms
◦ To avoid confusion we outlaw more than one arrow
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Use of entity sets vs. attributes
Choice mainly depends on the structure of the
enterprise being modeled, and on the semantics
associated with the attribute in question.
Use of entity sets vs. relationship sets
Possible guideline is to designate a relationship set
to describe an action that occurs between entities
Binary versus n-ary relationship sets
Although it is possible to replace any nonbinary (nary, for n > 2) relationship set by a number of
distinct binary relationship sets, a n-ary
relationship set shows more clearly that several
entities participate in a single relationship.
Placement of relationship attributes
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Some relationships that appear to be nonbinary may be better represented using binary
relationships
◦ E.g. A ternary relationship parents, relating a child
to his/her father and mother, is best replaced by two
binary relationships, father and mother
 Using two binary relationships allows partial information
(e.g. only mother being know)
◦ But there are some relationships that are naturally
non-binary
 Example: works_on
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In general, any non-binary relationship can be represented using binary
relationships by creating an artificial entity set.
◦ Replace R between entity sets A, B and C by an entity set E, and three
relationship sets:
1. RA, relating E and A
2.RB, relating E and B
3. RC, relating E and C
◦ Create a special identifying attribute for E
◦ Add any attributes of R to E
◦ For each relationship (ai , bi , ci) in R, create
1. a new entity ei in the entity set E
2. add (ei , ai ) to RA
3. add (ei , bi ) to RB
4. add (ei , ci ) to RC
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Also need to translate constraints
◦ Translating all constraints may not be possible
◦ There may be instances in the translated schema
that
cannot correspond to any instance of R
 Exercise: add constraints to the relationships RA, RB
and RC to ensure that a newly created entity
corresponds to exactly one entity in each of entity
sets A, B and C
◦ We can avoid creating an identifying attribute by
making E a weak entity set (described shortly)
identified by the three relationship sets
 Can make access-date an attribute of account, instead of a relationship
attribute, if each account can have only one customer
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That is, the relationship from account to customer is many to one, or
equivalently, customer to account is one to many
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An entity set that does not have a primary key is
referred to as a weak entity set.
The existence of a weak entity set depends on the
existence of a identifying entity set
◦ it must relate to the identifying entity set via a total,
one-to-many relationship set from the identifying to the
weak entity set
◦ Identifying relationship depicted using a double
diamond
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The discriminator (or partial key) of a weak entity
set is the set of attributes that distinguishes
among all the entities of a weak entity set.
The primary key of a weak entity set is formed by
the primary key of the strong entity set on which
the weak entity set is existence dependent, plus
the weak entity set’s discriminator.
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We depict a weak entity set by double rectangles.
We underline the discriminator of a weak entity set
with a dashed line.
payment_number – discriminator of the payment
entity set
Primary key for payment – (loan_number,
payment_number)
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Note: the primary key of the strong entity
set is not explicitly stored with the weak
entity set, since it is implicit in the
identifying relationship.
If loan_number were explicitly stored,
payment could be made a strong entity, but
then the relationship between payment and
loan would be duplicated by an implicit
relationship defined by the attribute
loan_number common to payment and loan
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In a university, a course is a strong entity and a
course_offering can be modeled as a weak
entity
The discriminator of course_offering would be
semester (including year) and section_number
(if there is more than one section)
If we model course_offering as a strong entity
we would model course_number as an attribute.
Then the relationship with course would be
implicit in the course_number attribute
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Top-down design process; we designate
subgroupings within an entity set that are
distinctive from other entities in the set.
These subgroupings become lower-level entity sets
that have attributes or participate in relationships
that do not apply to the higher-level entity set.
Depicted by a triangle component labeled ISA (E.g.
customer “is a” person).
Attribute inheritance – a lower-level entity set
inherits all the attributes and relationship
participation of the higher-level entity set to which
it is linked.
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A bottom-up design process – combine a
number of entity sets that share the same
features into a higher-level entity set.
Specialization and generalization are simple
inversions of each other; they are represented
in an E-R diagram in the same way.
The terms specialization and generalization
are used interchangeably.
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Can have multiple specializations of an entity
set based on different features.
E.g. permanent_employee vs.
temporary_employee, in addition to officer vs.
secretary vs. teller
Each particular employee would be
◦ a member of one of permanent_employee or
temporary_employee,
◦ and also a member of one of officer, secretary, or
teller
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The ISA relationship also referred to as
superclass - subclass relationship
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Constraint on which entities can be
members of a given lower-level entity set.
◦ condition-defined
 Example: all customers over 65 years are members of
senior-citizen entity set; senior-citizen ISA person.
◦ user-defined
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Constraint on whether or not entities may
belong to more than one lower-level entity
set within a single generalization.
◦ Disjoint
 an entity can belong to only one lower-level entity set
 Noted in E-R diagram by writing disjoint next to the
ISA triangle
◦ Overlapping
 an entity can belong to more than one lower-level
entity set
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Completeness constraint -- specifies
whether or not an entity in the higherlevel entity set must belong to at least
one of the lower-level entity sets within
a generalization.
◦ total : an entity must belong to one of the
lower-level entity sets
◦ partial: an entity need not belong to one of
the lower-level entity sets
 Consider the ternary relationship works_on, which we saw earlier
 Suppose we want to record managers for tasks performed by an
employee at a branch
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Relationship sets works_on and manages represent
overlapping information
◦ Every manages relationship corresponds to a works_on
relationship
◦ However, some works_on relationships may not correspond
to any manages relationships
 So we can’t discard the works_on relationship
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Eliminate this redundancy via aggregation
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Without introducing redundancy, the following
diagram represents:
◦ Treat relationship as an abstract entity
◦ Allows relationships between relationships
◦ Abstraction of relationship into new entity
◦ An employee works on a particular job at a particular
branch
◦ An employee, branch, job combination may have an
associated manager
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The use of an attribute or entity set to
represent an object.
Whether a real-world concept is best
expressed by an entity set or a relationship
set.
The use of a ternary relationship versus a
pair of binary relationships.
The use of a strong or weak entity set.
The use of specialization/generalization –
contributes to modularity in the design.
The use of aggregation – can treat the
aggregate entity set as a single unit without
concern for the details of its internal
structure.
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Primary keys allow entity sets and
relationship sets to be expressed
uniformly as relation schemas that
represent the contents of the
database.
A database which conforms to an E-R
diagram can be represented by a
collection of schemas.
For each entity set and relationship
set there is a unique schema that is
assigned the name of the
corresponding entity set or
relationship set.
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A strong entity set reduces to a schema with the same
attributes.
A weak entity set becomes a table that includes a column for
the primary key of the identifying strong entity set
payment =
( loan_number, payment_number, payment_date,
payment_amount )
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A many-to-many relationship set is represented as a
schema with attributes for the primary keys of the two
participating entity sets, and any descriptive attributes of
the relationship set.
Example: schema for relationship set borrower
borrower = (customer_id, loan_number )
 Many-to-one and one-to-many relationship sets that are total on the
many-side can be represented by adding an extra attribute to the
“many” side, containing the primary key of the “one” side
 Example: Instead of creating a schema for relationship set
account_branch, add an attribute branch_name to the schema
arising from entity set account
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For one-to-one relationship sets, either
side can be chosen to act as the “many”
side
◦ That is, extra attribute can be added to either of
the tables corresponding to the two entity sets
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If participation is partial on the “many”
side, replacing a schema by an extra
attribute in the schema corresponding to
the “many” side could result in null values
The schema corresponding to a
relationship set linking a weak entity set to
its identifying strong entity set is
redundant.
◦ Example: The payment schema already contains
the attributes that would appear in the
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Composite attributes are flattened out by
creating a separate attribute for each
component attribute
◦ Example: given entity set customer with composite
attribute name with component attributes
first_name and last_name the schema
corresponding to the entity set has two attributes
name.first_name and name.last_name
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A multivalued attribute M of an entity E is
represented by a separate schema EM
◦ Schema EM has attributes corresponding to the
primary key of E and an attribute corresponding to
multivalued attribute M
◦ Example: Multivalued attribute dependent_names
of employee is represented by a schema:
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Method 1:
◦ Form a schema for the higher-level entity
◦ Form a schema for each lower-level entity set,
include primary key of higher-level entity set
and local attributes
schema
attributes
person
name, street, city
customer name, credit_rating
employee name, salary
◦ Drawback: getting information about, an
employee requires accessing two relations, the
one corresponding to the low-level schema
and the one corresponding to the high-level
schema
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Method 2:
◦ Form a schema for each entity set with all local and
inherited attributes
schema
person
customer
employee
attributes
name, street, city
name, street, city, credit_rating
name, street, city, salary
◦ If specialization is total, the schema for the
generalized entity set (person) not required to store
information
 Can be defined as a “view” relation containing union of
specialization relations
 But explicit schema may still be needed for foreign key
 To represent aggregation, create a schema containing
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primary key of the aggregated relationship,
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the primary key of the associated entity set
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any descriptive attributes
 For example, to represent aggregation manages between
relationship works_on and entity set manager, create a schema
manages (employee_id, branch_name, title, manager_name)
 Schema works_on is redundant provided we are willing to store null
values for attribute manager_name in relation on schema manages
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UML: Unified Modeling Language
UML has many components to graphically
model different aspects of an entire
software system
UML Class Diagrams correspond to E-R
Diagram, but several differences.
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Entity sets are shown as boxes, and
attributes are shown within the box,
rather than as separate ellipses in E-R
diagrams.
Binary relationship sets are represented in
UML by just drawing a line connecting the
entity sets. The relationship set name is
written adjacent to the line.
The role played by an entity set in a
relationship set may also be specified by
writing the role name on the line, adjacent
to the entity set.
The relationship set name may
overlapping
disjoint
*Note reversal of position in cardinality constraint depiction
*Generalization can use merged or separate arrows independent
of disjoint/overlapping
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Cardinality constraints are specified in
the form l..h, where l denotes the
minimum and h the maximum number
of relationships an entity can
participate in.
Beware: the positioning of the
constraints is exactly the reverse of the
positioning of constraints in E-R
diagrams.
The constraint 0..* on the E2 side and
0..1 on the E1 side means that each E2
entity can participate in at most one
relationship, whereas each E1 entity
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If the existence of entity x depends on
the existence of entity y, then x is said
to be existence dependent on y.
◦ y is a dominant entity (in example below,
loan)
◦ x is a subordinate entity (in example below,
payment
)
loan
payment
loan-payment
If a loan entity is deleted, then all its associated payment entities
must be deleted also.